Crop row detection is an integral part of the open-field navigation of agricultural robots. Contour tracing or edge detection is also a popular method of crop row detection despite the availability of colour segmentation-based approaches such as [1]}, [2]}. The contour detection approach requires adequate image filtering for noise reduction and adjusting the algorithm thresholds to adapt to the varying lighting conditions [3]}. Although this approach is viable under known lighting conditions, further research must be conducted on the dynamic threshold setting under varying ambient lighting. [4]} also discussed the use of Hough transform [5]} in crop row detection. The Hough transform-based crop row detection only limits to straight crop rows whereas crop rows with a curvature will be approximated to a straight line. This limitation could be mitigated at the cost of limiting the row detection region of the image when performing the Hough transform. However, executing Hough transform on a realtime frame-by-frame basis is a highly computationally intensive task. Hence a robot with Hough transform-based crop row detection may need an expensive and powerful embedded computation unit on board. In addition to the above considerations, the speed of the robot, speed of image acquisition, computational time and delays of execution in motor control must be accounted for when developing the autonomous navigation algorithms for agricultural robots.
